We recently advanced the Canadian Fire Weather Index (FWI) system for northern peatlands by integrating peatland-specific hydrological data derived from assimilating Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperature observations into the NASA catchment model with its peatland modules ‘PEATCLSM’ (Mortelmans et al. 2024). This novel FWIpeat was evaluated using satellite-based fire presence data over boreal peatlands from 2010 through 2018, demonstrating improved estimation of peatland fire presence. Here, we will go beyond the previous use of the peatland-specific hydrological data in the established FWI system. We will show preliminary results of using this data in a machine learning algorithm to further understand when, where, and why peatlands burn. Leveraging several additional predictor variables, such as peatland distribution characteristics, vegetation properties, socioeconomic variables and lightning observations, we train a random forest algorithm with peatland fire occurrence data to investigate driving factors of peatland fires. This approach enables proactive fire risk management strategies and contributes to a comprehensive assessment of peatland fire vulnerability and resilience. Mortelmans, J., Felsberg, A., De Lannoy, G. J. M., Veraverbeke, S., Field, R. D., Andela, N., and Bechtold, M.: Improving the fire weather index system for peatlands using peat-specific hydrological input data, Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, 2024.